Numpy¶
Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.
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# install numpy
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pip install numpy
Requirement already satisfied: numpy in c:\users\dell\anaconda3\lib\site-packages (1.26.4) Note: you may need to restart the kernel to use updated packages.
To use Numpy, we first need to import the numpy package. By convention, we
import it using the alias np. Then, when we want to use modules or functions in this library, we preface them with np.
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import numpy as np
We can create a numpy array by passing a Python list to np.array().
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a = np.array([1, 2, 3]) # Create a rank 1 array
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print(type(a), a.shape, a[0], a[1], a[2])
a[0] = 5 # Change an element of the array
print(a)
<class 'numpy.ndarray'> (3,) 1 2 3 [5 2 3]
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b = np.array([[1,2,3],[3,4,3]]) # Create a rank 2 array
print(b)
[[1 2 3] [3 4 3]]
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print(b[1][1])
4
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print(b.shape)
(2, 3)
There are often cases when we want numpy to initialize the values of the
array for us. numpy provides methods like ones(), zeros(), and random.random() for these cases. We just pass them the number of elements we want it to generate:
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a = np.zeros((2,2)) # Create an array of all zeros
print(a)
[[0. 0.] [0. 0.]]
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b = np.ones((1,2)) # Create an array of all ones
print(b)
[[1. 1.]]
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c = np.full((2,3), 9) # Create a constant array
print(c)
[[9 9 9] [9 9 9]]
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e = np.random.random((2,2)) # Create an array filled with random values
print(e)
[[0.48000656 0.21906727] [0.99940097 0.76741752]]
Matplotlib¶
Matplotlib is a plotting library. In this section we give a brief introduction to the matplotlib.pyplot module, which provides a plotting system similar to that of MATLAB.
By convention, we typically import this module using the plt alias:
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# install matplotlib
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pip install matplotlib
Requirement already satisfied: matplotlib in c:\users\dell\anaconda3\lib\site-packages (3.8.4) Requirement already satisfied: contourpy>=1.0.1 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (1.2.0) Requirement already satisfied: cycler>=0.10 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (0.11.0) Requirement already satisfied: fonttools>=4.22.0 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (4.51.0) Requirement already satisfied: kiwisolver>=1.3.1 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (1.4.4) Requirement already satisfied: numpy>=1.21 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (1.26.4) Requirement already satisfied: packaging>=20.0 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (23.2) Requirement already satisfied: pillow>=8 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (10.3.0) Requirement already satisfied: pyparsing>=2.3.1 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (3.0.9) Requirement already satisfied: python-dateutil>=2.7 in c:\users\dell\anaconda3\lib\site-packages (from matplotlib) (2.9.0.post0) Requirement already satisfied: six>=1.5 in c:\users\dell\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0) Note: you may need to restart the kernel to use updated packages.
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import matplotlib.pyplot as plt
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print(3 * np.pi)
9.42477796076938
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# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)
# Plot the points using matplotlib
plt.plot(x, y)
# Show the figure.
plt.show()
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print(x)
[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3. 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4. 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5. 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6. 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7. 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8. 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9. 9.1 9.2 9.3 9.4]
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y_sin = np.sin(x)
y_cos = np.cos(x)
# Plot the points using matplotlib
plt.plot(x, y_sin, label='Sine')
plt.plot(x, y_cos, label='Cosine')
plt.legend() # uses the label arguments given above
plt.xlabel('x axis label')
plt.ylabel('y axis label')
plt.title('Sine and Cosine')
# Show the figure.
plt.show()
pandas¶
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# install pandas
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pip install pandas
Requirement already satisfied: pandas in c:\users\dell\anaconda3\lib\site-packages (2.2.2) Requirement already satisfied: numpy>=1.26.0 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (1.26.4) Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (2024.1) Requirement already satisfied: tzdata>=2022.7 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (2023.3) Requirement already satisfied: six>=1.5 in c:\users\dell\anaconda3\lib\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0) Note: you may need to restart the kernel to use updated packages.
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# Import the pandas library
import pandas as pd
# Create a sample DataFrame with names
data = {
'First Name': ['John', 'Sarah', 'Michael', 'Emily', 'David'],
'Last Name': ['Doe', 'Smith', 'Johnson', 'Wilson', 'Lee'],
"age":[21,22,23,24,25]
}
df = pd.DataFrame(data)
# Display the DataFrame
print(df)
First Name Last Name age 0 John Doe 21 1 Sarah Smith 22 2 Michael Johnson 23 3 Emily Wilson 24 4 David Lee 25
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df['Full Name'] = df['First Name'] + ' ' + df['Last Name']
print(df)
First Name Last Name age Full Name 0 John Doe 21 John Doe 1 Sarah Smith 22 Sarah Smith 2 Michael Johnson 23 Michael Johnson 3 Emily Wilson 24 Emily Wilson 4 David Lee 25 David Lee
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pip install openpyxl
Requirement already satisfied: openpyxl in c:\users\dell\anaconda3\lib\site-packages (3.1.2) Requirement already satisfied: et-xmlfile in c:\users\dell\anaconda3\lib\site-packages (from openpyxl) (1.1.0) Note: you may need to restart the kernel to use updated packages.
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# Save the DataFrame to an Excel file
df.to_excel('names_data2.xlsx', index=False)
object detection¶
Face mask detection¶
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#Cvlib is A simple, high level, easy-to-use open source Computer Vision library for Python.
#Face detection Detecting faces in an image is as simple as just calling the function detect_face().
#Object detection Detecting common objects in the scene is enabled through a single function call detect_common_objects().
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pip install cvlib
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pip install opencv-python
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pip install tensorflow
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import cv2
import cvlib as cv
from cvlib.object_detection import draw_bbox
from PIL import Image
from IPython import display
import numpy as np
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# Read the image into a numpy array
filename="C:\\Users\\dell\\OneDrive\\Desktop\\hh.jpg"
img = cv2.imread(filename)
img
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array([[[ 28, 49, 70],
[ 27, 48, 69],
[ 26, 50, 70],
...,
[153, 159, 158],
[153, 159, 158],
[153, 158, 157]],
[[ 28, 49, 70],
[ 28, 49, 70],
[ 26, 50, 70],
...,
[151, 156, 157],
[151, 156, 157],
[151, 156, 157]],
[[ 27, 51, 71],
[ 26, 50, 70],
[ 27, 50, 72],
...,
[150, 157, 160],
[153, 158, 161],
[154, 159, 162]],
...,
[[ 20, 20, 20],
[ 15, 15, 15],
[ 14, 14, 14],
...,
[ 70, 103, 159],
[ 60, 94, 153],
[ 66, 96, 153]],
[[ 19, 21, 21],
[ 14, 16, 16],
[ 13, 15, 15],
...,
[ 71, 102, 153],
[ 62, 95, 151],
[ 66, 96, 151]],
[[ 21, 23, 23],
[ 14, 16, 16],
[ 13, 15, 15],
...,
[ 69, 99, 146],
[ 65, 96, 149],
[ 67, 95, 149]]], dtype=uint8)
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im = Image.open(filename)
im.show()
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print("OpenCV version:", cv2.__version__)
OpenCV version: 4.10.0
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bbox, label, conf = cv.detect_common_objects(img)
# Print detected objects with confidence level
for b, l, c in zip(bbox,label, conf):
print(f"Detected object: {l} with confidence level of {c}, bbox: {b}\n")
# Create a new image that includes the bounding boxes
output_image = draw_bbox(img, bbox, label, conf)
# Save the image in the directory images_with_boxes
cv2.imwrite(f'images_with_box.jpg', output_image)
--------------------------------------------------------------------------- error Traceback (most recent call last) Cell In[29], line 1 ----> 1 bbox, label, conf = cv.detect_common_objects(img) 3 # Print detected objects with confidence level 4 for b, l, c in zip(bbox,label, conf): File ~\anaconda3\Lib\site-packages\cvlib\object_detection.py:125, in detect_common_objects(image, confidence, nms_thresh, model, enable_gpu) 123 if initialize: 124 classes = populate_class_labels() --> 125 net = cv2.dnn.readNet(weights_file_abs_path, config_file_abs_path) 126 initialize = False 128 # enables opencv dnn module to use CUDA on Nvidia card instead of cpu error: OpenCV(4.10.0) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\darknet\darknet_io.cpp:705: error: (-215:Assertion failed) separator_index < line.size() in function 'cv::dnn::darknet::ReadDarknetFromCfgStream'
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im = Image.open("images_with_box.jpg")
im.show()
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--------------------------------------------------------------------------- error Traceback (most recent call last) Cell In[27], line 8 5 weights_file = 'path/to/your/weights.weights' 7 # Load the network architecture and weights ----> 8 net = cv2.dnn.readNetFromDarknet(config_file, weights_file) 10 # Optionally set preferable backend and target to improve performance 11 net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) error: OpenCV(4.10.0) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\darknet\darknet_importer.cpp:210: error: (-212:Parsing error) Failed to open NetParameter file: path/to/your/config.cfg in function 'cv::dnn::dnn4_v20240521::readNetFromDarknet'
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